Improved Label-Free Identification of Individual Exosome-like Vesicles with Au@Ag Nanoparticles as SERS Substrate

Emerging biomedical applications of surface-enhanced Raman spectroscopy integrated with artificial intelligence and microfluidic technologies

The integration of surface-enhanced Raman spectroscopy (SERS), artificial intelligence (AI), and microfluidics represent a transformative approach for biomedical applications. By combining the molecular sensitivity of SERS, AI-driven spectral analysis, and the precise sample handling of microfluidics, these novel integrated systems enable ultrasensitive, label-free diagnostics with minimal sample processing. The development of portable, cost-effective platforms could democratize advanced diagnostics for resource-limited settings. However, challenges such as reproducibility, clinical validation, and system integration hinder widespread adoption. This review explores these new integrated platforms, beginning with a discussion of SERS principles, their biomedical applications, and the critical roles of AI and microfluidics in enhancing analytical performance. We evaluate recent advances in the application of these integrated systems, while addressing key challenges such as substrate scalability, biocompatibility, and point-of-care translation, with a focus on nanomaterials, AI models, and lab-on-chip designs. Finally, we outline future directions, including multimodal sensing, sustainable materials, and embedded AI for real-time diagnostics, to bridge the gap between technological innovation and clinical implementation.

Surface-Enhanced Raman Spectroscopy for Biomedical Applications: Recent Advances and Future Challenges

The year 2024 marks the 50th anniversary of the discovery of surface-enhanced Raman spectroscopy (SERS). Over recent years, SERS has experienced rapid development and became a critical tool in biomedicine with its unparalleled sensitivity and molecular specificity. This review summarizes the advancements and challenges in SERS substrates, nanotags, instrumentation, and spectral analysis for biomedical applications. We highlight the key developments in colloidal and solid SERS substrates, with an emphasis on surface chemistry, hotspot design, and 3D hydrogel plasmonic architectures. Additionally, we introduce recent innovations in SERS nanotags, including those with interior gaps, orthogonal Raman reporters, and near-infrared-II-responsive properties, along with biomimetic coatings. Emerging technologies such as optical tweezers, plasmonic nanopores, and wearable sensors have expanded SERS capabilities for single-cell and single-molecule analysis. Advances in spectral analysis, including signal digitalization, denoising, and deep learning algorithms, have improved the quantification of complex biological data. Finally, this review discusses SERS biomedical applications in nucleic acid detection, protein characterization, metabolite analysis, single-cell monitoring, and in vivo deep Raman spectroscopy, emphasizing its potential for liquid biopsy, metabolic phenotyping, and extracellular vesicle diagnostics. The review concludes with a perspective on clinical translation of SERS, addressing commercialization potentials and the challenges in deep tissue in vivo sensing and imaging.

Surface-Enhanced Raman Scattering (SERS) for exosome detection

BACKGROUND

Exosomes, nanoscale extracellular vesicles secreted by various cells, are abundantly present in biological fluids. They have been identified as carriers of specific molecules, suggesting their potential role in early disease detection. However, their clinical application is hindered by several challenges, including the need for large sample volumes for enrichment, limitations of traditional detection methods, and the complexity involved in phenotype analysis and separation.

OBJECTIVE

This review aims to explore the application of Surface-Enhanced Raman Scattering (SERS) technology in exosome detection. SERS, known for its unique photonic properties and high sensitivity, offers a promising solution for detecting exosomes without the need for large sample volumes or extensive phenotypic analysis. This review focuses on the real-time and non-invasive assessment capabilities of SERS in exosome detection, providing insights into its potential for early disease diagnosis.

CONCLUSION

The review concludes by emphasizing the potential of SERS-based exosome detection in advancing early disease diagnosis. By overcoming existing challenges, SERS technology offers a promising approach for the development of sensitive and specific diagnostic assays, contributing to better patient outcomes and personalized medicine.

Raman spectroscopy in extracellular vesicles analysis: Techniques, applications and advancements

Raman spectroscopy provides a robust approach for detailed analysis of the chemical and molecular profiles of extracellular vesicles (EVs). Recent advancements in Raman techniques have significantly enhanced the sensitivity and accuracy of EV characterization, enabling precise detection and profiling of molecular components within EV samples. This review introduces and compares various Raman-based techniques for EV characterization. These include Raman spectroscopy (RS), which provides fundamental molecular information; Raman trapping analysis (RTA), which combines optical trapping with Raman scattering for the manipulation and analysis of individual EVs; surface-enhanced Raman spectroscopy (SERS), which enhances the Raman signal through the use of metallic nanostructures, significantly improving sensitivity; and microfluidic SERS, which integrates SERS with microfluidic platforms to allow high-throughput, label-free analysis of EVs in biological fluids. In addition to comparing various Raman techniques, this review provides a comprehensive analysis that includes comparisons of machine learning methods, EV isolation techniques, and characterization strategies. By integrating these approaches, the review presents a holistic perspective on Raman-based EV analysis, covering profiling, purity, heterogeneity and size analysis as well as imaging. The combined assessment of Raman technologies with advanced computational and experimental methodologies supports the development of more robust diagnostic and therapeutic applications involving EVs.

Label-Free SERS Analysis of Biological and Physical Information Heterogeneity of Nanoscale Extracellular Vesicle by Matching Specific Sizes of Enhanced Particles

The heterogeneity of extracellular vesicles (EVs) surface information represents different functions, which is neglected in previous studies. In this study, a label-free SERS analysis approach is demonstrated to study fundamental EV biological and physical information heterogeneity by matching specific sizes of nano-enhanced particles. This strategy reveals informative, comprehensive, and high-quality SERS spectra of the overall exosome surface, and effectively circumvents the key information loss caused by the spatial resistance of NPs binding to the 293 exosomes' concave structure. The classification of normal and cancerous cell-derived exosomes by PCA method, the accuracy is improved from 91.2% to 95.1% by optimizing sizes of nano-enhanced particles. In addition, stem cell-derived EVs of diverse sizes and morphologies similarly show acuity of spectrum variation to NPs size, which is conductive to qualitative studies. This new strategy will offer a widened in-depth understanding of the surface information, size, and morphology of EVs, which can be applied to the study of biological functions.

Functional Immunoaffinity 3D Magnetic Core-Shell Nanometallic Structure for High-Efficiency Separation and Label-Free SERS Detection of Exosomes

Tumor exosomes, known as maternal cell messengers, play an important role in cancer occurrence, proliferation, metastasis, immune escape, drug resistance, and other processes and are an entry point for cancer research. However, there is still a lack of an efficient detection technology for exosomes. In this study, the ultrahigh sensitivity SERS nanoprobe with a three dimensional (3D) magnetic core/Au nanocolumn/Au nanoparticles shell strongly coupling multistage structure (Fe3O4@NR-NPs) was constructed by crystal growth of nanocrystals in the confined space of a central radiating single particle mesoporous molecular sieve channel and strong coupling secondary growth of gold particles. The exosomes were confined onto the "hot spot" of plasmonic nanoparticles and rapidly enriched by CD63 antibody functional-Fe3O4@NR-NPs to achieve high sensitivity detection, with the limit of detection of 1 × 103 particles/mL (S/N = 3). The spectral data set of different exosomes is applied to train for multivariate classification of cell types and to estimate how the normal exosome data resemble cancer cell exosomes by principal component analysis (PCA). Finally, this detection method has also been successfully employed for the detection of exosomes in complex samples; this proves that the proposed SERS-based method is a promising tool for clinical cancer screening.

Advances in exosome plasmonic sensing: Device integration strategies and AI-aided diagnosis

Exosomes, as next-generation biomarkers, has great potential in tracking cancer progression. They face many detection limitations in cancer diagnosis. Plasmonic biosensors have attracted considerable attention at the forefront of exosome detection, due to their label-free, real-time, and high-sensitivity features. Their advantages in multiplex immunoassays of minimal liquid samples establish the leading position in various diagnostic studies. This review delineates the application principles of plasmonic sensing technologies, highlighting the importance of exosomes-based spectrum and image signals in disease diagnostics. It also introduces advancements in miniaturizing plasmonic biosensing platforms of exosomes, which can facilitate point-of-care testing for future healthcare. Nowadays, inspired by the surge of artificial intelligence (AI) for science and technology, more and more AI algorithms are being adopted to process the exosome spectrum and image data from plasmonic detection. Using representative algorithms of machine learning has become a mainstream trend in plasmonic biosensing research for exosome liquid biopsy. Typically, these algorithms process complex exosome datasets efficiently and establish powerful predictive models for precise diagnosis. This review further discusses critical strategies of AI algorithm selection in exosome-based diagnosis. Particularly, we categorize the AI algorithms into the interpretable and uninterpretable groups for exosome plasmonic detection applications. The interpretable AI enhances the transparency and reliability of diagnosis by elucidating the decision-making process, while the uninterpretable AI provides high diagnostic accuracy with robust data processing by a "black-box" working mode. We believe that AI will continue to promote significant progress of exosome plasmonic detection and mobile healthcare in the near future.

Surface-enhanced Raman scattering-based strategies for tumor markers detection: A review

The presence of malignant tumors poses a significant threat to people's life and well-being. As biochemical parameters indicate the occurrence and development of tumors, tumor markers play a pivotal role in early cancer detection, treatment, prognosis, efficient monitoring, and other aspects. Surface-enhanced Raman scattering (SERS) is considered a potent tool for the detection of tumor markers owing to its exceptional advantages encompassing high sensitivity, superior selectivity, rapid analysis speed, and photobleaching resistance nature. This review aims to provide a comprehensive understanding of SERS applications in the detection of tumor markers. Firstly, we introduce the SERS enhancement mechanism, classification of active substrates, and SERS detection techniques. Secondly, the latest research progress of in vitro SERS detection of different types of tumor markers in body fluids and the application of SERS imaging in biomedical imaging are highlighted in sections of the review. Finally, according to the current status of SERS detection of tumor markers, the challenges and problems of SERS in biomedical detection are discussed, and insights into future developments in SERS are offered.

Label-Free Exosome Analysis by Surface-Enhanced Raman Scattering Spectroscopy with Laser-Ablated Silver Nanoparticle Substrate

Early diagnostics of breast cancer is crucial to reduce the risk of cancer metastasis and late relapse. Exosome, which contains distinct information of its origin, can be the target object as a liquid biopsy. However, its low sensitivity and inadequate diagnostic tools interfere with the point-of-care testing (POCT) of the exosome. Recently, Surface-enhanced Raman Scattering (SERS) spectroscopy, which amplifies the Raman scattering, has been proved as a promising tool for exosome detection. However, the fabrication process of SERS probe or substrate is still inefficient and far from large-scale production. This study proposes rapid and label-free detection of breast cancer-derived exosomes by statistical analysis of SERS spectra using silver-nanoparticle-based SERS substrate fabricated by selective laser ablation and melting (SLAM). Employing silver nanowires and optimizing laser process parameters enable rapid and low-energy fabrication of SERS substrate. The functionalities including sensitivity, reproducibility, stability, and renewability are evaluated using rhodamine 6G as a probe molecule. Then, the feasibility of POCT is examined by the statistical analysis of SERS spectra of exosomes from malignant breast cancer cells and non-tumorigenic breast epithelial cells. The presented framework is anticipated to be utilized in other biomedical applications, facilitating cost-effective and large-scale production performance.

Label-free optical detection of calcium ion influx in cell-derived nanovesicles using a conical Au/PDMS biosensor

Ion channels, which are key to physiological regulation and drug discovery, control ion flux across membranes, and their dysregulation leads to various diseases. Ca2+ monitoring is crucial for cellular signaling when performing Ca-based assays in ion channel research; these assays are widely utilized in both academic and pharmaceutical contexts for drug screening and pharmacological profiling. However, existing detection methods are limited by slow detection speeds, low throughput, complex processes, and low analyte viability. In this study, we developed a label-free optical biosensing method using a conical Au/polydimethylsiloxane platform tailored to detect Ca2+ influx in A549-originated nanovesicles facilitated by the transient receptor potential ankyrin 1 (TRPA1) channel. Nanovesicles expressing cellular signaling components mimic TRPA1 signal transduction in cell membranes and improve analyte viability. The conical Au/polydimethylsiloxane sensor converted Ca2+ influx events induced by specific agonist exposure into noticeable changes in relative transmittance under visible light. The optical transmittance change accompanying Ca2+ influx resulted in an enhanced sensing response, high accuracy and reliability, and rapid detection (∼5 s) without immobilization or ligand treatments. In the underlying sensing mechanism, morphological variations in nanovesicles, which depend on Ca2+ influx, induce a considerable light scattering change at an interface between the nanovesicle and Au, revealed by optical simulation. This study provides a foundation for developing biosensors based on light-matter interactions. These sensors are simple and cost-effective with superior performance and diverse functionality.

Effective enrichment of trace exosomes for the label-free SERS detection via low-cost thermophoretic profiling

Exosome-based liquid biopsies possess great potential in monitoring cancer development However, current exosome detection biosensors require large exosome volumes, showing the weak detection sensitivity. Besides, these methods pay little attention to in situ analysis of exosomes, hence limiting the provision of more accurate clinically-relevant information. Herein, we develop an innovative label-free biosensor combining the low-cost thermophoretic enrichment method with the surface-enhanced Raman spectroscopy (SERS) detection. Based on the thermophoretic enrichment strategy, exosomes and gold nanoparticles can be enriched together into a small area with a scale of 500 μm within 10 min. The Raman signals of various exosomes derived from normal, cancerous cell lines and human serum are dynamically monitored in situ, with the limit of detection of 102-103 particles per microliter, presenting higher sensitivity compared with the similar label-free SERS detection. The spectral data set of different exosomes is applied to train for multivariate classification of cell types and to estimate how the normal exosome data resemble cancer cell exosome. The reliable classification and identification of different exosomes can be realized. The current biosensor is convenient, low-cost and requires small exosome volumes (∼3 μL), and if validated in larger cohorts may contribute to the tumor prediction and diagnosis.

Increasing the sensitivity and accuracy of detecting exosomes as biomarkers for cancer monitoring using optical nanobiosensors

The advancement of nanoscience and material design in recent times has facilitated the creation of point-of-care devices for cancer diagnosis and biomolecule sensing. Exosomes (EXOs) facilitate the transfer of bioactive molecules between cancer cells and diverse cells in the local and distant microenvironments, thereby contributing to cancer progression and metastasis. Specifically, EXOs derived from cancer are likely to function as biomarkers for early cancer detection due to the genetic or signaling alterations they transport as payload within the cancer cells of origin. It has been verified that EXOs circulate steadily in bodily secretions and contain a variety of information that indicates the progression of the tumor. However, acquiring molecular information and interactions regarding EXOs has presented significant technical challenges due to their nanoscale nature and high heterogeneity. Colorimetry, surface plasmon resonance (SPR), fluorescence, and Raman scattering are examples of optical techniques utilized to quantify cancer exosomal biomarkers, including lipids, proteins, RNA, and DNA. Many optically active nanoparticles (NPs), predominantly carbon-based, inorganic, organic, and composite-based nanomaterials, have been employed in biosensing technology. The exceptional physical properties exhibited by nanomaterials, including carbon NPs, noble metal NPs, and magnetic NPs, have facilitated significant progress in the development of optical nanobiosensors intended for the detection of EXOs originating from tumors. Following a summary of the biogenesis, biological functions, and biomarker value of known EXOs, this article provides an update on the detection methodologies currently under investigation. In conclusion, we propose some potential enhancements to optical biosensors utilized in detecting EXO, utilizing various NP materials such as silicon NPs, graphene oxide (GO), metal NPs, and quantum dots (QDs).

Advances of machine learning-assisted small extracellular vesicles detection strategy

Detection of extracellular vesicles (EVs), particularly small EVs (sEVs), is of great significance in exploring their physiological characteristics and clinical applications. The heterogeneity of sEVs plays a crucial role in distinguishing different types of cells and diseases. Machine learning, with its exceptional data processing capabilities, offers a solution to overcome the limitations of conventional detection methods for accurately classifying sEV subtypes and sources. Principal component analysis, linear discriminant analysis, partial least squares discriminant analysis, XGBoost, support vector machine, k-nearest neighbor, and deep learning, along with some combined methods such as principal component-linear discriminant analysis, have been successfully applied in the detection and identification of sEVs. This review focuses on machine learning-assisted detection strategies for cell identification and disease prediction via sEVs, and summarizes the integration of these strategies with surface-enhanced Raman scattering, electrochemistry, inductively coupled plasma mass spectrometry and fluorescence. The performance of different machine learning-based detection strategies is compared, and the advantages and limitations of various machine learning models are also evaluated. Finally, we discuss the merits and limitations of the current approaches and briefly outline the perspective of potential research directions in the field of sEV analysis based on machine learning.

Urease-Powered Micromotors with Spatially Selective Distribution of Enzymes for Capturing and Sensing Exosomes

Enzyme-catalyzed micro/nanomotors (MNMs) exhibit tremendous potential for biological isolation and sensing, because of their biocompatibility, versatility, and ready access to biofuel. However, flow field generated by enzyme-catalyzed reactions might significantly hinder performance of surface-linked functional moieties, e.g., the binding interaction between MNMs and target cargos. Herein, we develop enzymatic micromotors with spatially selective distribution of urease to enable the independent operation of various modules and facilitate the capture and sensing of exosomes. When urease is modified into the motors' cavity, the flow field from enzyme catalysis has little effect on the exterior surface of the motors. The active motion and encapsulating urease internally result in enhancement of ∼35% and 18% in binding efficiency of target cargos, e.g., exosomes as an example here, compared to their static counterparts and moving micromotors with urease modified externally, respectively. Once exosomes are trapped, they can be transferred to a clean environment by the motors for Raman signal detection and/or identification using the surface Raman enhancement scattering (SERS) effect of coated gold nanoshell. The biocatalytic micromotors, achieving spatial separation between driving module and function module, offer considerable promise for future design of multifunctional MNMs in biomedicine and diagnostics.

Detection of micro-plasma-induced exosomes secretion in a fibroblast-melanoma co-culture model

BACKGROUND

Melanoma is a highly aggressive tumor and a significant cause of skin cancer-related death. Timely diagnosis and treatment require identification of specific biomarkers in exosomes secreted by melanoma cells. In this study, label-free surface-enhanced Raman spectroscopy (SERS) method with size-matched selectivity was used to detect membrane proteins in exosomes released from a stimulated environment of fibroblasts (L929) co-cultured with melanoma cells (B16-F10). To promote normal secretion of exosomes, micro-plasma treatment was used to gently induce the co-cultured cells and slightly increase the stress level around the cells for subsequent detection using the SERS method.

RESULTS AND DISCUSSION

Firstly, changes in reactive oxygen species/reactive nitrogen species (ROS/RNS) concentrations in the cellular microenvironment and the viability and proliferation of healthy cells are assessed. Results showed that micro-plasma treatment increased extracellular ROS/RNS levels while modestly reducing cell proliferation without significantly affecting cell survival. Secondly, the particle size of secreted exosomes isolated from the culture medium of L929, B16-F10, and co-cultured cells with different micro-plasma treatment time did not increase significantly under single-cell conditions at short treatment time but might be changed under co-culture condition or longer treatment time. Third, for SERS signals related to membrane protein biomarkers, exosome markers CD9, CD63, and CD81 can be assigned to significant Raman shifts in the range of 943-1030 and 1304-1561 cm-1, while the characteristics SERS peaks of L929 and B16-F10 cells are most likely located at 1394/1404, 1271 and 1592 cm-1 respectively.

SIGNIFICANCE AND NOVELTY

Therefore, this micro-plasma-induced co-culture model provides a promising preclinical approach to understand the diagnostic potential of exosomes secreted by cutaneous melanoma/fibroblasts. Furthermore, the label-free SERS method with size-matched selectivity provides a novel approach to screen biomarkers in exosomes secreted by melanoma cells, aiming to reduce the use of labeling reagents and the processing time traditionally required.

Extracellular Vesicles: Techniques and Biomedical Applications Related to Single Vesicle Analysis

Extracellular vesicles (EVs) are extensively dispersed lipid bilayer membrane vesicles involved in the delivery and transportation of molecular payloads to certain cell types to facilitate intercellular interactions. Their significant roles in physiological and pathological processes make EVs outstanding biomarkers for disease diagnosis and treatment monitoring as well as ideal candidates for drug delivery. Nevertheless, differences in the biogenesis processes among EV subpopulations have led to a diversity of biophysical characteristics and molecular cargos. Additionally, the prevalent heterogeneity of EVs has been found to substantially hamper the sensitivity and accuracy of disease diagnosis and therapeutic monitoring, thus impeding the advancement of clinical applications. In recent years, the evolution of single EV (SEV) analysis has enabled an in-depth comprehension of the physical properties, molecular composition, and biological roles of EVs at the individual vesicle level. This review examines the sample acquisition tactics prior to SEV analysis, i.e., EV isolation techniques, and outlines the current state-of-the-art label-free and label-based technologies for SEV identification. Furthermore, the challenges and prospects of biomedical applications based on SEV analysis are systematically discussed.

MoS2-Plasmonic Nanocavities for Raman Spectra of Single Extracellular Vesicles Reveal Molecular Progression in Glioblastoma

Extracellular vesicles (EVs) are continually released from cancer cells into biofluids, carrying actionable molecular fingerprints of the underlying disease with considerable diagnostic and therapeutic potential. The scarcity, heterogeneity and intrinsic complexity of tumor EVs present a major technological challenge in real-time monitoring of complex cancers such as glioblastoma (GBM). Surface-enhanced Raman spectroscopy (SERS) outputs a label-free spectroscopic fingerprint for EV molecular profiling. However, it has not been exploited to detect known biomarkers at the single EV level. We developed a multiplex fluidic device with embedded arrayed nanocavity microchips (MoSERS microchip) that achieves 97% confinement of single EVs in a minute amount of fluid (<10 μL) and enables molecular profiling of single EVs with SERS. The nanocavity arrays combine two featuring characteristics: (1) An embedded MoS₂ monolayer that enables label-free isolation and nanoconfinement of single EVs due to physical interaction (Coulomb and van der Waals) between the MoS₂ edge sites and the lipid bilayer; and (2) A layered plasmonic cavity that enables sufficient electromagnetic field enhancement inside the cavities to obtain a single EV level signal resolution for stratifying the molecular alterations. We used the GBM paradigm to demonstrate the diagnostic potential of the SERS single EV molecular profiling approach. The MoSERS multiplexing fluidic achieves parallel signal acquisition of glioma molecular variants (EGFRvIII oncogenic mutation and MGMT expression) in GBM cells. The detection limit of 1.23% was found for stratifying these key molecular variants in the wild-type population. When interfaced with a convolutional neural network (CNN), MoSERS improved diagnostic accuracy (87%) with which GBM mutations were detected in 12 patient blood samples, on par with clinical pathology tests. Thus, MoSERS demonstrates the potential for molecular stratification of cancer patients using circulating EVs.

Novel strategies in melanoma treatment using silver nanoparticles

Melanoma has remarkably gained extensive attention owing to its high morbidity and mortality. Conventional treatment methods still have some problems and defects. Therefore, more and more novel methods and materials have been continuously developed. Silver nanoparticles (AgNPs) have attracted significant interest in the field of cancer research especially for melanoma treatment because of their excellent properties including antioxidant, antiproliferative, anti-inflammatory, antibacterial, antifungal, and antitumor abilities. In this review, the applications of AgNPs in the prevention, diagnosis, and treatment of cutaneous melanoma are mainly introduced. It also focuses on the therapy strategies of photodynamic therapy (PDT), photothermal therapy (PTT), and chemotherapy for melanoma treatment. Taken together, AgNPs play an increasingly crucial role in cutaneous melanoma treatment, which have promising application in the future.

Label-Free Identification of Exosomes using Raman Spectroscopy and Machine Learning

Exosomes, nano-sized extracellular vesicles (EVs) secreted from cells, carry various cargo molecules reflecting their cells of origin. As EV content, structure, and size are highly heterogeneous, their classification via cargo molecules by determining their origin is challenging. Here, a method is presented combining surface-enhanced Raman spectroscopy (SERS) with machine learning algorithms to employ the classification of EVs derived from five different cell lines to reveal their cellular origins. Using an artificial neural network algorithm, it is shown that the label-free Raman spectroscopy method's prediction ratio correlates with the ratio of HT-1080 exosomes in the mixture. This machine learning-assisted SERS method enables a new direction through label-free investigation of EV preparations by differentiating cancer cell-derived exosomes from those of healthy. This approach will potentially open up new avenues of research for early detection and monitoring of various diseases, including cancer.

Recent advances of Au@Ag core-shell SERS-based biosensors

The methodological advancements in surface-enhanced Raman scattering (SERS) technique with nanoscale materials based on noble metals, Au, Ag, and their bimetallic alloy Au-Ag, has enabled the highly efficient sensing of chemical and biological molecules at very low concentration values. By employing the innovative various type of Au, Ag nanoparticles and especially, high efficiency Au@Ag alloy nanomaterials as substrate in SERS based biosensors have revolutionized the detection of biological components including; proteins, antigens antibodies complex, circulating tumor cells, DNA, and RNA (miRNA), etc. This review is about SERS-based Au/Ag bimetallic biosensors and their Raman enhanced activity by focusing on different factors related to them. The emphasis of this research is to describe the recent developments in this field and conceptual advancements behind them. Furthermore, in this article we apex the understanding of impact by variation in basic features like effects of size, shape varying lengths, thickness of core-shell and their influence of large-scale magnitude and morphology. Moreover, the detailed information about recent biological applications based on these core-shell noble metals, importantly detection of receptor binding domain (RBD) protein of COVID-19 is provided.

Nanostructured-Based Optical Readouts Interfaced with Machine Learning for Identification of Extracellular Vesicles

Extracellular vesicles (EVs) are shed from cancer cells into body fluids, enclosing molecular information about the underlying disease with the potential for being the target cancer biomarker in emerging diagnosis approaches such as liquid biopsy. Still, the study of EVs presents major challenges due to their heterogeneity, complexity, and scarcity. Recently, liquid biopsy platforms have allowed the study of tumor-derived materials, holding great promise for early-stage diagnosis and monitoring of cancer when interfaced with novel adaptations of optical readouts and advanced machine learning analysis. Here, recent advances in labeled and label-free optical techniques such as fluorescence, plasmonic, and chromogenic-based systems interfaced with nanostructured sensors like nanoparticles, nanoholes, and nanowires, and diverse machine learning analyses are reviewed. The adaptability of the different optical methods discussed is compared and insights are provided into prospective avenues for the translation of the technological approaches for cancer diagnosis. It is discussed that the inherent augmented properties of nanostructures enhance the sensitivity of the detection of EVs. It is concluded by reviewing recent integrations of nanostructured-based optical readouts with diverse machine learning models as novel analysis ventures that can potentially increase the capability of the methods to the point of translation into diagnostic applications.

Hierarchy of hybrid materials. Part-II: The place of organics-on-inorganics in it, their composition and applications

Hybrid materials or hybrids incorporating organic and inorganic constituents are emerging as a very potent and promising class of materials due to the diverse but complementary nature of their properties. This complementarity leads to a perfect synergy of properties of the desired materials and products as well as to an extensive range of their application areas. Recently, we have overviewed and classified hybrid materials describing inorganics-in-organics in Part-I (Saveleva, et al., Front. Chem., 2019, 7, 179). Here, we extend that work in Part-II describing organics-on-inorganics, i.e., inorganic materials modified by organic moieties, their structure and functionalities. Inorganic constituents comprise of colloids/nanoparticles and flat surfaces/matrices comprise of metallic (noble metal, metal oxide, metal-organic framework, magnetic nanoparticles, alloy) and non-metallic (minerals, clays, carbons, and ceramics) materials; while organic additives can include molecules (polymers, fluorescence dyes, surfactants), biomolecules (proteins, carbohydtrates, antibodies and nucleic acids) and even higher-level organisms such as cells, bacteria, and microorganisms. Similarly to what was described in Part-I, we look at similar and dissimilar properties of organic-inorganic materials summarizing those bringing complementarity and composition. A broad range of applications of these hybrid materials is also presented whose development is spurred by engaging different scientific research communities.

Nanomaterials meet surface-enhanced Raman scattering towards enhanced clinical diagnosis: a review

Surface-enhanced Raman scattering (SERS) is a very promising tool for the direct detection of biomarkers for the diagnosis of i.e., cancer and pathogens. Yet, current SERS strategies are hampered by non-specific interactions with co-existing substances in the biological matrices and the difficulties of obtaining molecular fingerprint information from the complex vibrational spectrum. Raman signal enhancement is necessary, along with convenient surface modification and machine-based learning to address the former issues. This review aims to describe recent advances and prospects in SERS-based approaches for cancer and pathogens diagnosis. First, direct SERS strategies for key biomarker sensing, including the use of substrates such as plasmonic, semiconductor structures, and 3D order nanostructures for signal enhancement will be discussed. Secondly, we will illustrate recent advances for indirect diagnosis using active nanomaterials, Raman reporters, and specific capture elements as SERS tags. Thirdly, critical challenges for translating the potential of the SERS sensing techniques into clinical applications via machine learning and portable instrumentation will be described. The unique nature and integrated sensing capabilities of SERS provide great promise for early cancer diagnosis or fast pathogens detection, reducing sanitary costs but most importantly allowing disease prevention and decreasing mortality rates.

Nanomaterials assisted exosomes isolation and analysis towards liquid biopsy

Exosomes has attracted tremendous research interests as they are emerging as a new paradigm of liquid biopsy. Although the concentration of exosomes in blood is relatively abundant, there still exists various vesicle-like nanoparticles, such as microvesicles, apoptotic bodies. It's an urgent need to isolate and enrich exosomes from the complex contaminants in biofluid samples. Moreover, the expressing level of exosomal biomarkers varies a lot, which make the sensitive molecular detection of exosomes in high demand. Unfortunately, the efficient isolation and sensitive molecular quantification of exosomes is still a major obstacle hindering the further development and clinical application of exosome-based liquid biopsy. Nanomaterials, with unique physiochemical properties, have been widely used in biosensing and analysis aspects, thus they are thought as powerful tools for effective purification and molecular analysis of exosomes. In this review, we summarized the most recent progresses in nanomaterials assisted exosome isolation and analysis towards liquid biopsy. On the one hand, nanomaterials can be used as capture substrates to afford large binding area and specific affinity to exosomes. Meanwhile, nanomaterials can also be served as promising signal transducers and amplifiers for molecular detection of exosomes. Furthermore, we also pointed out several potential and promising research directions in nanomaterials assisted exosome analysis. It's envisioned that this review will give the audience a complete outline of nanomaterials in exosome study, and further promote the intersection of nanotechnology and bio-analysis.

Robust, reliable and quantitative sensing of aqueous arsenic species by Surface-enhanced Raman Spectroscopy: The crucial role of surface silver ions for good analytical practice

Arsenic speciation analysis is important for pollution and health risk assessment. Surface-enhanced Raman Spectroscopy (SERS) is supposed to be a promising detection technology for arsenic species owing to the unique fingerprints. However, further application of SERS is hampered by its poor repeatability. Herein, the role of surface silver ions on colloidal Ag was revealed in SERS analysis of arsenic species. Arsenic species were adsorbed on Ag nanoparticles (Ag NPs) driven by surface silver ions and were simultaneously sensed by the SERS "hot spots" generated from the aggregation of Ag NPs. So, the inconsistent SERS activities of Ag NPs synthesized from different batches can be significantly improved by modifying external silver ions onto Ag NPs (AgNPs@Ag⁺), Specific binding affinity of surface silver ions to arsenic species generated higher sensitivity (detection limit, 4.0 × 10^-11 mol L^-1 for arsenite, 8.0 × 10^-11 mol L^-1 for arsenate), wider linear range, faster response, cleaner spectra background and better reproducibility. Batch-to-batch reproducibility was significantly improved with a variation below 3.1%. The method was also demonstrated with drinking and environmental water with adequate recovery and high interference resistance. Our findings displayed good analytical practice of the surface silver ions derived SERS method and its great potential in the rapid detection of hazardous materials.

Recent progress in surface-enhanced Raman spectroscopy-based biosensors for the detection of extracellular vesicles

Extracellular vesicles (EVs) are a type of mediator that enables intercellular communication. Moreover, EVs carry critical molecular information from parental cells, making them ideal biomarkers for clinical screening and diagnosis. Currently, several sensing technologies have been established to sensitively detect EVs. Among them, surface-enhanced Raman spectroscopy (SERS) has become a powerful analytical tool with high sensitivity and low detection limits. In this review, we first cover the biological characteristics of EVs and the principle of SERS amplification. Then, we describe the recent progress in SERS technology applied to detect EVs, including direct label-free methods and indirect labeling strategies, in which substrate fabrication and nanoprobe assembly were emphasized. Furthermore, SERS technology could also be used to characterize or monitor the behavior of programmable EVs. Finally, we discuss the prospects and issues to be addressed for the development of SERS technology for EV analysis.

Emerging SERS biosensors for the analysis of cells and extracellular vesicles

Cells and their derived extracellular vesicles (EVs) or exosomes contain unique molecular signatures that could be used as biomarkers for the detection of severe diseases such as cancer, as well as monitoring the treatment response. Revealing these molecular signatures requires developing non-invasive ultrasensitive tools to enable single molecule/cell-level detection using a small volume of sample with low signal-to-noise ratio background and multiplex capability. Surface-enhanced Raman scattering (SERS) can address the current limitations in studying cells and EVs through two main mechanisms: plasmon-enhanced electric field (the so-called electromagnetic mechanism (EM)), and chemical mechanism (CM). In this review, we first highlight these two SERS mechanisms and then discuss the nanomaterials that have been used to develop SERS biosensors based on each of the aforementioned mechanisms as well as the combination of these two mechanisms in order to take advantage of the synergic effect between electromagnetic enhancement and chemical enhancement. Then, we review the recent advances in designing label-aided and label-free SERS biosensors in both colloidal and planar systems to investigate the surface biomarkers on cancer cells and their derived EVs. Finally, we discuss perspectives of emerging SERS biosensors in future biomedical applications. We believe this review article will thus appeal to researchers in the field of nanobiotechnology including material sciences, biosensors, and biomedical fields.

Artificial Intelligent Label-Free SERS Profiling of Serum Exosomes for Breast Cancer Diagnosis and Postoperative Assessment

Breast cancer subtypes have important implications of treatment responses and clinical outcomes. Exosomes have been considered as promising biomarkers for liquid biopsies, but the utility of exosomes for accurate diagnosis of distinct breast cancer subtypes is a grand challenge due to the difficulty in uncovering the subtle compositional difference in complex clinical settings. Herein, we report an artificial intelligent surface-enhanced Raman spectroscopy (SERS) strategy for label-free spectroscopic analysis of serum exosomes, allowing for accurate diagnosis of breast cancer and assessment of surgical outcomes. Our deep learning algorithm trained with SERS spectra of cancer cell-derived exosomes is demonstrated with a 100% prediction accuracy for human patients with different breast cancer subtypes who do not undergo surgery using SERS spectra of serum exosomes. Furthermore, when combined with similarity analysis by principal component analysis, our approach is able to evaluate the surgical outcomes of breast cancer of distinct molecular subtypes.

Recent advances in optical label-free characterization of extracellular vesicles

Extracellular vesicles (EVs) are complex biological nanoparticles endogenously secreted by all eukaryotic cells. EVs carry a specific molecular cargo of proteins, lipids, and nucleic acids derived from cells of origin and play a significant role in the physiology and pathology of cells, organs, and organisms. Upon release, they may be found in different body fluids that can be easily accessed via noninvasive methodologies. Due to the unique information encoded in their molecular cargo, they may reflect the state of the parent cell and therefore EVs are recognized as a rich source of biomarkers for early diagnostics involving liquid biopsy. However, body fluids contain a mixture of EVs released by different types of healthy and diseased cells, making the detection of the EVs of interest very challenging. Recent research efforts have been focused on the detection and characterization of diagnostically relevant subpopulations of EVs, with emphasis on label-free methods that simplify sample preparation and are free of interfering signals. Therefore, in this paper, we review the recent progress of the label-free optical methods employed for the detection, counting, and morphological and chemical characterization of EVs. We will first briefly discuss the biology and functions of EVs, and then introduce different optical label-free techniques for rapid, precise, and nondestructive characterization of EVs such as nanoparticle tracking analysis, dynamic light scattering, atomic force microscopy, surface plasmon resonance spectroscopy, Raman spectroscopy, and SERS spectroscopy. In the end, we will discuss their applications in the detection of neurodegenerative diseases and cancer and provide an outlook on the future impact and challenges of these technologies to the field of liquid biopsy via EVs.

Exosome detection via surface-enhanced Raman spectroscopy for cancer diagnosis

As nanoscale extracellular vesicles, exosomes are secreted by various cell types, and they are widely distributed in multiple biological fluids. Studies have shown that tumor-derived exosomes can carry a variety of primary tumor-specific molecules, which may represent a novel tool for the early detection of cancer. However, the clinical translation of exosomes remains a challenge due to the requirement of large quantities of samples when enriching the cancer-related exosomes in biological fluids, the insufficiency of traditional techniques for exosome subpopulations, and the complex exosome isolation of the current commercially available exosome phenotype profiling approaches. The evolving surface-enhanced Raman scattering (SERS) technology, with properties of unique optoelectronics, easy functionalization, and the particular interaction between light and nanoscale metallic materials, can achieve sensitive detection of exosomes without large quantities of samples and multiplexed phenotype profiling, providing a new mode of real-time and noninvasive analysis for cancer patients. In the present review, we mainly discussed exosome detection based on SERS, especially SERS immunoassay. The basic structure and function of exosomes were firstly introduced. Then, recent studies using the SERS technique for cancer detection were critically reviewed, which mainly included various SERS substrates, biological modification of SERS substrates, SERS-based exosome detection, and the combination of SERS and other technologies for cancer diagnosis. This review systematically discussed the essential aspects, limitations, and considerations of applying SERS technology in the detection and analysis of cancer-derived exosomes, which could provide a valuable reference for the early diagnosis of cancer through SERS technology. STATEMENT OF SIGNIFICANCE: Surface-enhanced Raman scattering (SERS) has been applied to exosomes detection to obtain better diagnostic results. In past three years, several reviews have been published in exosome detection, which were narrowly focus on methods of exosome detection. Selection and surface functionalization of the substrate and the combination detection with different methods based on SERS will provide new strategies for the detection of exosomes. This review will focus on the above aspects. This emerging detection method is constantly evolving and contributing to the early discovery of diseases in the future.

Surface enhanced Raman scattering for probing cellular biochemistry

Surface enhanced Raman scattering (SERS) from biomolecules in living cells enables the sensitive, but also very selective, probing of their biochemical composition. This minireview discusses the developments of SERS probing in cells over the past years from the proof-of-principle to observe a biochemical status to the characterization of molecule-nanostructure and molecule-molecule interactions and cellular processes that involve a wide variety of biomolecules and cellular compartments. Progress in applying SERS as a bioanalytical tool in living cells, to gain a better understanding of cellular physiology and to harness the selectivity of SERS, has been achieved by a combination of live cell SERS with several different approaches. They range from organelle targeting, spectroscopy of relevant molecular models, and the optimization of plasmonic nanostructures to the application of machine learning and help us to unify the information from defined biomolecules and from the cell as an extremely complex system.

Visualized SERS Imaging of Single Molecule by Ag/Black Phosphorus Nanosheets

Ag/BP-NS exhibit remarkable surface-enhanced Raman scattering performance with single-molecule detection ability. This remarkable enhancement can be attributed to the synergistic resonance enhancement of R6G molecular resonance, photo-induced charge transfer resonance and electromagnetic resonance. A new polarization-mapping method was proposed, which can quickly screen out single-molecule signals and prove that the obtained spectra are emitted by single molecule. The recognition of different tumor exosomes can be realized combining the method of machine learning.

ABSTRACT

Single-molecule detection and imaging are of great value in chemical analysis, biomarker identification and other trace detection fields. However, the localization and visualization of single molecule are still quite a challenge. Here, we report a special-engineered nanostructure of Ag nanoparticles embedded in multi-layer black phosphorus nanosheets (Ag/BP-NS) synthesized by a unique photoreduction method as a surface-enhanced Raman scattering (SERS) sensor. Such a SERS substrate features the lowest detection limit of 10^–20 mol L⁻¹ for R6G, which is due to the three synergistic resonance enhancement of molecular resonance, photo-induced charge transfer resonance and electromagnetic resonance. We propose a polarization-mapping strategy to realize the detection and visualization of single molecule. In addition, combined with machine learning, Ag/BP-NS substrates are capable of recognition of different tumor exosomes, which is meaningful for monitoring and early warning of the cancer. This work provides a reliable strategy for the detection of single molecule and a potential candidate for the practical bio-application of SERS technology. [Image: see text]

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40820-022-00803-x.

Controlling the Interaction between Fluorescent Gold Nanoclusters and Biointerfaces for Rapid Discrimination of Fungal Pathogens

Nondestructive detection and discrimination of fungal pathogens is essential for rapid and precise treatment, which further effectively prevents antifungal resistance from overused drugs. In this work, fluorescent gold nanoclusters served as the basis for discriminating Candida species. Varied on surface ligands, these gold nanoclusters demonstrated different optical properties as a result of the perturbation effects of ligands. The biointerface interaction between the surface ligands of gold nanoclusters and the cell walls of Candida species can be constructed, and their restriction on ligands perturbation effect produced enhanced fluorescence signals. Owing to the variation of the cell wall composition, cells of different Candida species demonstrated different degrees of association with the gold nanoclusters, leading to discriminable amounts of fluorescence enhancements. The reverse signal response from these gold nanoclusters gives rise to a synergistic and effective assay that allows identification of Candida species.

Advances in the Application of Exosomes Identification Using Surface-Enhanced Raman Spectroscopy for the Early Detection of Cancers

Exosomes are small nanoscale vesicles with a double-layered lipid membrane structure secreted by cells, and almost all types of cells can secrete exosomes. Exosomes carry a variety of biologically active contents such as nucleic acids and proteins, and play an important role not only in intercellular information exchange and signal transduction, but also in various pathophysiological processes in the human body. Surface-enhanced Raman Spectroscopy (SERS) uses light to interact with nanostructured materials such as gold and silver to produce a strong surface plasmon resonance effect, which can significantly enhance the Raman signal of molecules adsorbed on the surface of nanostructures to obtain a rich fingerprint of the sample itself or Raman probe molecules with ultra-sensitivity. The unique advantages of SERS, such as non-invasive and high sensitivity, good selectivity, fast analysis speed, and low water interference, make it a promising technology for life science and clinical testing applications. In this paper, we briefly introduce exosomes and the current main detection methods. We also describe the basic principles of SERS and the progress of the application of unlabeled and labeled SERS in exosome detection. This paper also summarizes the value of SERS-based exosome assays for early tumor diagnosis.

Positively-charged plasmonic nanostructures for SERS sensing applications

Surface-enhanced Raman (SERS) spectroscopy has been establishing itself as an ultrasensitive analytical technique with a cross-disciplinary range of applications, which scientific growth is triggered by the continuous improvement in the design of advanced plasmonic materials with enhanced multifunctional abilities and tailorable surface chemistry. In this regard, conventional synthetic procedures yield negatively-charged plasmonic materials which can hamper the adhesion of negatively-charged species. To tackle this issue, metallic surfaces have been modified via diverse procedures with a broad array of surface ligands to impart positive charges. Cationic amines have been preferred because of their ability to retain a positive zeta potential even at alkaline pH as well as due to their wide accessibility in terms of structural features and cost. In this review, we will describe and discuss the different approaches for generating positively-charged plasmonic platforms and their applications in SERS sensing.

Characterization of ovarian cancer-derived extracellular vesicles by surface-enhanced Raman spectroscopy

Ovarian cancer is the most lethal gynecological malignancy, owing to the fact that most cases are diagnosed at a late stage. To improve prognosis and reduce mortality, we must develop methods for the early diagnosis of ovarian cancer. A step towards early and non-invasive cancer diagnosis is through the utilization of extracellular vesicles (EVs), which are nanoscale, membrane-bound vesicles that contain proteins and genetic material reflective of their parent cell. Thus, EVs secreted by cancer cells can be thought of as cancer biomarkers. In this paper, we present gold nanohole arrays for the capture of ovarian cancer (OvCa)-derived EVs and their characterization by surface-enhanced Raman spectroscopy (SERS). For the first time, we have characterized EVs isolated from two established OvCa cell lines (OV-90, OVCAR3), two primary OvCa cell lines (EOC6, EOC18), and one human immortalized ovarian surface epithelial cell line (hIOSE) by SERS. We subsequently determined their main compositional differences by principal component analysis and were able to discriminate the groups by a logistic regression-based machine learning method with ∼99% accuracy, sensitivity, and specificity. The results presented here are a great step towards quick, facile, and non-invasive cancer diagnosis.

Advanced Nanotechnologies for Extracellular Vesicle-Based Liquid Biopsy

Extracellular vesicles (EVs) are emerging as a new source of biomarkers in liquid biopsy because of their wide presence in most body fluids and their ability to load cargoes from disease-related cells. Owing to the crucial role of EVs in disease diagnosis and treatment, significant efforts have been made to isolate, detect, and analyze EVs with high efficiency. A recent overview of advanced EV detection nanotechnologies is discussed here. First, several key challenges in EV-based liquid biopsies are introduced. Then, the related pivotal advances in nanotechnologies for EV isolation based on physical features, chemical affinity, and the combination of nanostructures and chemical affinity are summarized. Next, a summary of high-sensitivity sensors for EV detection and advanced approaches for single EV detection are provided. Later, EV analysis is introduced in practical clinical scenarios, and the application of machine learning in this field is highlighted. Finally, future opportunities for the development of next-generation nanotechnologies for EV detection are presented.

Nanozyme Sensor Array Plus Solvent-Mediated Signal Amplification Strategy for Ultrasensitive Ratiometric Fluorescence Detection of Exosomal Proteins and Cancer Identification

Tumor exosomes with molecular marker-proteins inherited from their parent cells have emerged as a promising liquid biopsy biomarker for cancer diagnosis. However, facile, robust, and sensitive detection of exosomal proteins remains challenging. Therefore, a nanozyme sensor array is constructed by using aptamer-modified C₃N₄ nanosheets (Apt/C₃N₄ NSs) together with a solvent-mediated signal amplification strategy for ratiometric fluorescence detection of exosomal proteins. Three aptamers specific to exosomal proteins are selected to construct Apt/C₃N₄ NSs for high specific recognition of exosomal proteins. The adsorption of aptamers enhances the catalytic activity of C₃N₄ NSs as a nanozyme for oxidation of o-phenylenediamine (oPD) to 2,3-diaminophenazine (DAP). In the presence of target exosomes, the strong affinity between aptamer and exosome leads to the disintegration of Apt/C₃N₄ NSs, resulting in a decrease of catalytic activity, thereby reducing the production of DAP. The ratiometric fluorescence signal based on a photoinduced electron transfer (PET) effect between DAP and C₃N₄ NSs is dependent on the concentration of DAP generated, thus achieving highly facile and robust detection of exosomal proteins. Remarkably, the addition of organic solvent-1,4-dioxane can sensitize the luminescence of DAP without affecting the intrinsic fluorescence of C₃N₄ NSs, achieving the amplification of the aptamer-exosome recognition events. The detection limit for exosome is 2.5 × 10³ particles/mL. In addition, the accurate identification of cancer can be achieved by machine learning algorithms to analyze the difference of exosomal proteins from different patients' blood. We hope that this facile, robust, sensitive, and versatile nanozyme sensor array would become a promising tool in the field of cancer diagnosis.

Surface-Enhanced Raman Scattering (SERS) Spectroscopy for Sensing and Characterization of Exosomes in Cancer Diagnosis

Simple Summary

The distinct molecular and biological properties of exosomes, together with their abundance and stability, make them an ideal target in liquid biopsies for early diagnosis and disease monitoring. On the other hand, in recent years, nanomaterial-based optical biosensors have been extensively investigated as novel, rapid and sensitive tools for exosome detection and discrimination. The scope of this review is to summarize and coherently discussed the diverse applications, challenges and limitations of nanosensors based on surface-enhanced Raman spectroscopy (SERS) as the optosensing technique.

Abstract

Exosomes are emerging as one of the most intriguing cancer biomarkers in modern oncology for early cancer diagnosis, prognosis and treatment monitoring. Concurrently, several nanoplasmonic methods have been applied and developed to tackle the challenging task of enabling the rapid, sensitive, affordable analysis of exosomes. In this review, we specifically focus our attention on the application of plasmonic devices exploiting surface-enhanced Raman spectroscopy (SERS) as the optosensing technique for the structural interrogation and characterization of the heterogeneous nature of exosomes. We summarized the current state-of-art of this field while illustrating the main strategic approaches and discuss their advantages and limitations.

Superhydrophobic bowl-like SERS substrates patterned from CMOS sensors for extracellular vesicle characterization

Using a regular CMOS sensor as a template, we are able to fabricate a simple but highly effective superhydrophobic SERS substrate. Specifically, we decorated the microlens layer of the sensor with 7 μm polystyrene beads to obtain a PDMS patterned replica. The process resulted in a uniform pattern of voids in the PDMS (denoted nanobowls) that are intercalated with a few larger voids (denoted here microbowls). The voids act as superhydrophobic substrates with analyte concentration capabilities in bigger bowl-like structures. Silver nanoparticles were directly grown on the patterned PDMS substrate inside both the nano- and microbowls, and serve as strong electromagnetic field enhancers for the SERS substrate. After systematic characterization of the fabricated SERS substrate by atomic force microscopy and scanning electron microscopy, we demonstrated its SERS performance using 4-aminothiophenol as a reporter molecule. Finally, we employed this innovative substrate to concentrate and analyze extracellular vesicles (EVs) isolated from an MC65 neural cell line in an ultralow sample volume. This substrate can be further exploited for the investigation of various EV biomarkers for early diagnosis of different diseases using liquid biopsy.

Single microbead-based fluorescent aptasensor (SMFA) for direct isolation and in situ quantification of exosomes from plasma

Exosomes are cell-derived membrane-enclosed biological nanoparticles that carry lots of parental molecular information, and are recognized as an ideal biomarker for non-invasive diagnosis. However, due to the low abundance of exosomes in plasma samples and the interferences from complex biological matrices, the sensitive and direct detection of exosomes still remains a challenge. Here, by combining the direct magnetic isolation with in situ fluorescence imaging, we developed a Single Microbead-based Fluorescent Aptasensor (SMFA) for specific enrichment and sensitive quantification of exosomes from plasma. In the SMFA, a single aptamer-modified microbead (MB) served as the reaction carrier so that the specific exosomes inserted with a fluorescent anchor will be highly enriched on the single MB. By in situ fluorescence imaging to monitor the fluorescence signals on the single MB, sensitive detection of exosomes can be realized without the requirement of any signal amplification routes, and as low as 4.9 × 10⁴ particles per μL of exosomes could be simply detected. More importantly, the SMFA could be applied for direct detection of the exosomes from small amounts of clinical plasma samples without prior purification procedures, indicating its great potential applications in clinical diagnostics.

Emerging technologies and commercial products in exosome-based cancer diagnosis and prognosis

Academic and industrial groups worldwide have reported technological advances in exosome-based cancer diagnosis and prognosis. However, the potential translation of these emerging technologies for research and clinical settings remains unknown. This work overviews the role of exosomes in cancer diagnosis and prognosis, followed by a survey on emerging exosome technologies, particularly microfluidic advances for the isolation and detection of exosomes in cancer research. The advantages and drawbacks of each of the technologies used for the isolation, detection and engineering of exosomes are evaluated to address their clinical challenges for cancer diagnosis and prognosis. Furthermore, commercial platforms for exosomal detection and analysis are introduced, and their performance and impact on cancer diagnosis and prognosis are assessed. Also, the risks associated with the further development of the next generation of exosome devices are discussed. The outcome of this work could facilitate recognizing deliverable Exo-devices and technologies with unprecedented functionality and predictable manufacturability for the next-generation of cancer diagnosis and prognosis.

Plasmonic nanobowtiefluidic device for sensitive detection of glioma extracellular vesicles by Raman spectrometry

Cancer cells shed into biofluids extracellular vesicles (EVs) - nanoscale membrane particles carrying diagnostic information. EVs shed by heterogeneous populations of tumor cells offer a unique opportunity to access biologically important aspects of disease complexity. Glioblastoma (GBM) exemplifies cancers that are incurable, because their temporal dynamics and molecular complexity evade standard diagnostic methods and confound therapeutic efforts. Liquid biopsy based on EVs offers unprecedented real-time access to complex tumour signatures, but it is not used clinically due to inefficient testing methods. We report on a nanostructured microfluidic-device that employs SERS for unambiguous identification of EVs from different GBM cell populations. The device features fabless plasmonic nanobowties for label-free and non-immunological SERS detection of EVs. This nanobowtiefluidic device combines the advanced characteristics of plasmonic nanobowties with a high throughput sample-delivery system for concentration of the analytes in the vicinity of the detection site. We showed theoretically and experimentally that the fluidic device assists the monolayer distribution of the EVs, which dramatically increase the probability of EV's existence in the laser illumination area. In addition, the optimized fabless nanobowtie structures with an average electric field enhancement factor of 9 × 10⁵ achieve distinguishable and high intensity SERS signals. Using the nanobowtiefluidic and micro-Raman equipment, we were able to distinguish a library of peaks expressed in GBM EV subpopulations from two distinct glioblastoma cell lines (U373, U87) and compare them to those of non-cancerous glial EVs (NHA) and artificial homogenous vesicles (e.g. DOPC/Chol). This cost-effective and easy-to-fabricate SERS platform and a portable sample-delivery system for discerning the sub-population of GBM EVs and non-cancerous glial EVs may have broader applications to different types of cancer cells and their molecular/oncogenic signature.